1 research outputs found
Inferring and Learning from Neuronal Correspondences
We introduce and study methods for inferring and learning from
correspondences among neurons. The approach enables alignment of data from
distinct multiunit studies of nervous systems. We show that the methods for
inferring correspondences combine data effectively from cross-animal studies to
make joint inferences about behavioral decision making that are not possible
with the data from a single animal. We focus on data collection, machine
learning, and prediction in the representative and long-studied invertebrate
nervous system of the European medicinal leech. Acknowledging the computational
intractability of the general problem of identifying correspondences among
neurons, we introduce efficient computational procedures for matching neurons
across animals. The methods include techniques that adjust for missing cells or
additional cells in the different data sets that may reflect biological or
experimental variation. The methods highlight the value harnessing inference
and learning in new kinds of computational microscopes for multiunit
neurobiological studies